[COMPARISON TESTS] Use pre-computed values in librosa compat test (#4018)

Co-authored-by: Sam Anklesaria <[email protected]>
Co-authored-by: Nicolas Hug <[email protected]>

Author: samanklesaria

.github/scripts/unittest-linux/install.sh

@@ -102,7 +102,7 @@ fi
     pip install kaldi-io SoundFile librosa coverage pytest pytest-cov scipy expecttest unidecode inflect Pillow sentencepiece pytorch-lightning 'protobuf<4.21.0' demucs tinytag pyroomacoustics flashlight-text git+https://github.com/kpu/kenlm
 
     # TODO: might be better to fix the single call to `pip install` above
-    pip install "pillow<10.0" "scipy<1.10" "numpy<2.0"
+    pip install pillow scipy "numpy>=1.26"
 )
 # Install fairseq
 git clone https://github.com/pytorch/fairseq

.github/workflows/unittest-linux-gpu.yml

@@ -106,7 +106,7 @@ jobs:
         
         pip3 install parameterized requests
         pip3 install kaldi-io SoundFile librosa coverage pytest pytest-cov scipy expecttest unidecode inflect Pillow sentencepiece pytorch-lightning 'protobuf<4.21.0' demucs tinytag 
-        pip3 install "pillow<10.0" "scipy<1.10" "numpy<2.0"
+        pip3 install pillow scipy "numpy>=1.26"
 
         echo "::endgroup::"
         echo "::group::Run tests"

test/librosa_mock.py

@@ -0,0 +1,62 @@
+import re
+import os
+from pathlib import Path
+import torch
+
+def mock_function(f):
+    """
+    Create a mocked version of a function from the librosa library that loads a precomputed result
+    if it exists. The commented out part otherwise computes the result and saves it for future use.
+    This is used to compare torchaudio functionality to the equivalent functionalty in librosa without
+    depending on librosa after results are precomputed.
+    """
+    this_file = Path(__file__).parent.resolve()
+    expected_results_folder = this_file / "torchaudio_unittest" / "assets" / "librosa_expected_results"
+    def wrapper(request, *args, **kwargs):
+        mocked_results = expected_results_folder / f"{request}.pt"
+        return torch.load(mocked_results, weights_only=False)
+
+        # Old definition used for generation:
+        # if os.path.exists(mocked_results):
+        #     return torch.load(mocked_results, weights_only=False)
+        # import librosa
+        # result = eval(f)(*args, **kwargs)
+        # if request is not None:
+        #     mocked_results.parent.mkdir(parents=True, exist_ok=True)
+        #     torch.save(result, mocked_results)
+        # return result
+    return wrapper
+
+griffinlim = mock_function("librosa.griffinlim")
+
+mel = mock_function("librosa.filters.mel")
+
+power_to_db = mock_function("librosa.core.power_to_db")
+
+amplitude_to_db = mock_function("librosa.core.amplitude_to_db")
+
+phase_vocoder = mock_function("librosa.phase_vocoder")
+
+spectrogram = mock_function("librosa.core.spectrum._spectrogram")
+
+mel_spectrogram = mock_function("librosa.feature.melspectrogram")
+
+def _mfcc_from_waveform(waveform, sample_rate, n_fft, hop_length, n_mels, n_mfcc):
+    import librosa
+    melspec = librosa.feature.melspectrogram(
+        y=waveform[0].cpu().numpy(),
+        sr=sample_rate,
+        n_fft=n_fft,
+        win_length=n_fft,
+        hop_length=hop_length,
+        n_mels=n_mels,
+        htk=True,
+        norm=None,
+        pad_mode="reflect",
+    )
+    return librosa.feature.mfcc(S=librosa.core.power_to_db(melspec), n_mfcc=n_mfcc, dct_type=2, norm="ortho")
+
+mfcc_from_waveform = mock_function("_mfcc_from_waveform")
+
+
+spectral_centroid = mock_function("librosa.feature.spectral_centroid")